Identifying and Characterizing Whistler Waves in the Solar Wind Using Machine Learning

24 years of observation by the Wind spacecraft at 1 AU has yielded a rich array of high-resolution magnetic field data, where a large fraction displays small-scale structures. In particular, the solar wind is full of magnetosonic-whistler fluctuations and kinetic Alfven fluctuations that appear in both the field magnitude and its components. The nature of these fluctuations can be tied to the properties of other structures in the solar wind, such as shocks. Additionally, the dissipation properties of the fluctuations heat the solar wind and affect its long-term evolution. Knowing the relative occur r ence rate of different physical fluctuations, therefore, may have significant implications for our understanding of the morphology of the solar wind. As such, having a large collection of wave events would facilitate further study of the effects that each type of fluctuation has on solar wind dynamics. Given the breadth of magnetic field data available, machine learning is the most practical approach to classifying the myriad small-scale structures observed in the magnetic field data. To this end, a subset of current W ind data is labeled and used as a training set for a machine learning algorithm aimed at classifying small-scale structures. This algorithm can then be used to catalog the entire W ind magnetic field dataset.